Elisabetta Strazzera

Modeling Elicitation effects in contingent valuation studies: a Monte Carlo Analysis of the bivariate approach

Research in the area of discrete choice modelling can be split into two broad categories; applications accounting for the prevalence of unobserved interalternative correlation, and applications concerned with the representation of random inter-agent taste heterogeneity. The difference between these two phenomena is however not as clear-cut as this division might suggest, and there is in fact a high risk of confounding between the two phenomena. In this article, we investigate the potential of Mixed Generalised Extreme Value (GEV) models to simultaneously account for the two phenomena, using a Stated Preference (SP) dataset for mode-choice in Switzerland. Initial results using more basic modelling techniques reveal the presence of both correlation and random taste heterogeneity. The subsequent use of Mixed GEV models on this dataset leads to important gains in performance over the use of the more basic models. However, the results also show that, by simultaneously accounting for correlation and random taste heterogeneity, the scope to retrieve the individual phenomena is reduced. This shows that a failure to account for the potential impacts of either of the two phenomena can lead to erroneous conclusions about the existence of the other phenomenon. This is a strong indication that the use of Mixed GEV modD R A F T Page 1 November 26, 2004,A Monte Carlo analysis is conducted to assess the validity of the bivariate modeling approach for detection and correction of different forms of elicitation effects in Double Bound Contingent Valuation data. Alternative univariate and bivariate models are applied to several simulated data sets, each one characterized by a specific elicitation effect, and their performance is assessed using standard selection criteria. The bivariate models include the standard Bivariate Probit model, and an alternative specification, based on the Copula approach to multivariate modeling, which is shown to be useful in cases where the hypothesis of normality of the joint distribution is not supported by the data. It is found that the bivariate approach can effectively correct elicitation effects while maintaining an adequate level of efficiency in the estimation of the parameters of interest.

Modelling zero values and protest responses in contingent valuation surveys

In contingent valuation surveys the category of zero bidders refers to individuals that are not willing to pay anything for the programme under analysis. Specific questions can help to identify true zero values, coming from people that are indifferent to the programme, separately from protest responses: the latter are generally excluded from the analysis. This paper introduces a mixture-sample selection model that takes into account both zero values and protest responses in the estimates. The model is applied to the valuation of a traffic calming scheme aimed at reducing risks for residents in three villages in the north-east of England.

Bias and Efficiency of Single vs. Double Bound Models for Contingent Valuation Studies: a Monte Carlo Analysis

The Dichotomous Choice Contingent Valuation Method can be used either in the single or double bound formulation. The former is easier to implement, while the latter is known to be more efficient. We analyse the bias of the ML estimates produced by either model, and the gain in efficiency associated to the double bound model, in different experimental settings. We find that there are no relevant differences in point estimates given by the two models, even for small sample size, and no estimator can be said to be less biased than the other. The greater efficiency of the double bound is confirmed, although differences tend to reduce by increasing the sample size. Provided that a reliable pre-test is conducted, and the sample size is large, use of the single rather than the double bound model is warranted.

Examining Predictive Accuracy Among Discounting Models

Both descriptive and normative arguments claim that the discount rate to be applied to public projects should be elicited from individual intertemporal preferences. We present a methodology to analyze data from experimental surveys on intertemporal preferences. Focusing on the exponential and the hyperbolic discounting models, we model the experimental data published by Thaler (1981) by means of different specifications. Standard measures of goodness of prediction are then applied to fitted data to select among alternative specifications. We first present our approach by applying it to simulated data. We then present a procedure for statistical estimation of the sample discount rate, testing four specifications.

Applying the copula approach to sample selection modelling

The limited availability of tractable multivariate distributions undermines the validity of the standard parametric approach to sample selection modelling. Copula distributions can be very useful in situations where the applied researcher has a prior on the distributional form of the margins, since the modelling of the latter is separated from that of the dependence structure. The present article first presents an application to female work data. Afterwards, the approach is analysed in an application to contingent valuation data on recreational values of forests. It is shown that the copula approach is especially beneficial in case of strong departures from the hypothesis of normality.

A note about model selection and tests for non-nested contingent valuation models

Abstract The different approaches (Vuong’s model selection test and Cox’s non-nested hypothesis test) for the evaluation of non-nested, discrete choice models are analyzed, and their performance compared with Akaike’s selection criterion for application to simulated contingent valuation data.

Assessment of Regeneration Projects in Urban Areas of Environmental Interest: A Stated Choice Approach to Estimate Use and Quasi-Option Values

We adopt an attribute-based stated choice approach to evaluate public preferences over planning alternatives for an urban site of environmental interest. Since such projects involve some uncertainty and irreversibility, special attention is devoted to the estimation of quasi-option values associated with project development. Two distinct measures for the quasi-option value are estimated, and both coefficients indicate that the public place significant value on the reduction of the possibility of adverse irreversible effects: a more prudent development strategy is valued at about four times greater than a procedure that provides a greater chance of an undesired outcome. Furthermore, the study involves elicitation of intertemporal preferences over projects with different time spans, and estimation of the implicit discount rates: the values obtained seem high if compared with standard discount rates applied to public projects, but not far from interest rates on consumption found in the market.

The Effect of Protest Votes on the Estimates of Willingness to Pay for Use Values of Recreational Sites

Selectivity bias caused by protest responses in Contingent Valuation studies can be detected and corrected by means of sample selection models. This paper compares two methods: the Heckman 2-steps method and the full ML, applied to data on forest recreation - where WTP is elicited as a continuous variable. Either method has its own drawback: computational complexity for the ML method, susceptibility to collinearity problems for the 2-steps method. The latter problem is observed in our best fitting specification, with the ML estimator outperforming the 2-steps. In this application, overlooking the effect of protest responses would cause an upwards bias of the final estimates of WTP.

The Copula Approach to Sample Selection Modelling: An Application to the Recreational Value of Forests

The sample selection model is based upon a bivariate or a multivariate structure, and distributional assumptions are in this context more severe than in univariate settings, due to the limited availability of tractable multivariate distributions. While the standard FIML estimation of the selectivity model assumes normality of the joint distribution, alternative approaches require less stringent distributional hypotheses. As shown by Smith (2003), copulas allow great flexibility also in FIML models. The copula model is very useful in situations where the applied researcher has a prior on the distributional form of the margins, since it allows separating their modelling from that of the dependence structure. In the present paper the copula approach to sample selection is first compared to the semiparametric approach and to the standard FIML, bivariate normal model, in an illustrative application on female work data. Then its performance is analysed more thoroughly in an application to Contingent Valuation data on recreational values of forests.

Modelling Zero Bids in Contingent Valuation Surveys

When modelling data generated from a discrete choice contingent valuation question, the treatment of zero bids affects the welfare estimates. Zero bids may come from respondents who are not interested in the provision of the public good; alternatively, some zero-bidders may be protesting about the valuation exercise, but hold positive values for the good. In this paper we investigate the effect of different levels of information on zero-bidders on welfare estimates for the population. We find that different strategies of identification may have non-trivial effects. We recommend use of full debriefing questions for zero-bidders, and use of sample selection models to correct for bias caused by protest behaviour.